Sweden Compare Offical vs OWID Cases

rm(list=ls())
source("../DATA/movavg.R")
db <- db <- dbConnect(RSQLite::SQLite(),dbname= "../COVID-19-DB/OURWORLD.sqlite3")
SWE <- dbGetQuery(db,"select * from JHU where location ='Sweden'")

SWE$date <- as.Date(SWE$date)
SWE <- SWE[order(SWE$date),]

SWE$CMA <- ma(SWE$new_cases,6,centre=TRUE)
SWE$DMA <- ma(SWE$new_deaths,6,centre=TRUE)
dbDisconnect(db)

Sweden <- readxl::read_xlsx("./Folkhalsomyndigheten_Covid19.xlsx") %>%
  rename(Date = Statistikdatum) %>%
  rename(Cases = Totalt_antal_fall)

Sweden$Date <- as.Date(Sweden$Date)

Our World In Data: Sweden’s Daily Cases

SWE <- SWE %>% filter(date >="2020-11-1")
Sweden <- Sweden %>% filter(Date >="2020-11-1")
plot1 <- ggplot() + geom_col(data=SWE,aes(x=date,y=new_cases,col="OWID")) +
  geom_line(data=Sweden,aes(x=Date,y=Cases,col = "Offical")) +
  geom_line(data=SWE,aes(x=date,y=CMA,col = "6 Day Mov. Avg."))

ggplotly(plot1)

OUr World In Data: Sweden’s Daily Deaths

plot2 <- ggplot() + geom_col(data=SWE,aes(x=date,y=new_deaths,col="OWID")) +
geom_line(data=SWE,aes(x=date,y=DMA,col = "6 Day Mov. Avg."))

ggplotly(plot2)

Benford’s Law First Digit Analysis

set.seed(1234)
X <- rbenf(500)

signifd.analysis(X)

## $summary
##               1         2         3         4         5         6          7
## freq  0.2800000 0.1820000 0.1480000 0.0940000 0.0980000 0.0520000 0.03600000
## pvals 0.3052895 0.7286861 0.1188654 0.8259077 0.1191396 0.1811388 0.03538192
##               8         9
## freq  0.0540000 0.0560000
## pvals 0.7725734 0.2730563
## 
## $CIs
##               1         2          3          4          5          6
## 0.025 0.2608234 0.1427047 0.09595657 0.07097939 0.05551326 0.04503988
## 0.5   0.3010300 0.1760913 0.12493874 0.09691001 0.07918125 0.06694679
## 0.975 0.3412366 0.2094778 0.15392090 0.12284063 0.10284923 0.08885370
##                7          8          9
## 0.025 0.03750514 0.03184196 0.02744179
## 0.5   0.05799195 0.05115252 0.04575749
## 0.975 0.07847875 0.07046308 0.06407319

Sweden’s Cases

first_cases <- SWE$new_cases
signifd.analysis(first_cases)

## $summary
##                  1         2         3            4         5          6
## freq  1.011236e-01 0.1573034 0.1348315 0.2022471910 0.1235955 0.12359551
## pvals 3.933024e-05 0.6416925 0.7777473 0.0007818821 0.1207243 0.03249306
##                7          8          9
## freq  0.11235955 0.03370787 0.01123596
## pvals 0.02820367 0.45505825 0.11909962
## 
## $CIs
##               1          2          3          4          5          6
## 0.025 0.2057312 0.09695755 0.05624441 0.03544855 0.02308274 0.01502243
## 0.5   0.3010300 0.17609126 0.12493874 0.09691001 0.07918125 0.06694679
## 0.975 0.3963288 0.25522497 0.19363306 0.15837148 0.13527976 0.11887115
##                 7           8           9
## 0.025 0.009433561 0.005382111 0.002345113
## 0.5   0.057991947 0.051152522 0.045757491
## 0.975 0.106550333 0.096922934 0.089169868

Sweden’s Deaths

set.seed(4567)
first_deaths <- SWE$new_deaths
signifd.analysis(first_deaths)

## $summary
##                  1         2          3          4         5          6
## freq  1.011236e-01 0.1348315 0.07865169 0.07865169 0.1235955 0.07865169
## pvals 3.933024e-05 0.3068224 0.18661911 0.56040131 0.1207243 0.65862013
##                7            8          9
## freq  0.11235955 2.022472e-01 0.08988764
## pvals 0.02820367 9.792434e-11 0.04633051
## 
## $CIs
##               1          2          3          4          5          6
## 0.025 0.2057312 0.09695755 0.05624441 0.03544855 0.02308274 0.01502243
## 0.5   0.3010300 0.17609126 0.12493874 0.09691001 0.07918125 0.06694679
## 0.975 0.3963288 0.25522497 0.19363306 0.15837148 0.13527976 0.11887115
##                 7           8           9
## 0.025 0.009433561 0.005382111 0.002345113
## 0.5   0.057991947 0.051152522 0.045757491
## 0.975 0.106550333 0.096922934 0.089169868